Abstract: This paper presents INTRUSISHIELD, an intelligent, multi-layered Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) designed to navigate the evolving landscape of cyber threats. By integrating traditional rule-based methods with advanced machine learning algorithms, INTRUSISHIELD provides real-time monitoring and automated response capabilities to detect and mitigate both known and unknown threats. The system continuously updates its knowledge base to adapt to new attack patterns, ensuring robust network security. Additionally, INTRUSISHIELD incorporates a user-friendly Streamlit web application for easy monitoring and management of IDS functionalities. Extending this approach, a separate Streamlit app allows users to upload files for real-time detection of malicious content, enhancing the system’s preventive capabilities. This comprehensive solution demonstrates significant improvements in threat detection, mitigation, and user accessibility, thereby strengthening overall cybersecurity defenses.
Keywords: Intrusion Detection System, Intrusion Prevention System, Machine Learning, Cybersecurity, Real-time Monitoring, Streamlit
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DOI:
10.17148/IJARCCE.2024.13674
